Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. Pandas Series is nothing but a column in an excel sheet. In this article, we will see various ways of creating a series using different data types.
Creating Series from list
The list of some values form the series of that values uses list index as series index.
# import pandas as pd import pandas as pd
# simple list lst = [ 'G' , 'E' , 'E' , 'K' , 'S' , 'F' ,
'O' , 'R' , 'G' , 'E' , 'E' , 'K' , 'S' ]
# forming series s = pd.Series(lst)
# output print (s)
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Output :
0 G 1 E 2 E 3 K 4 S 5 F 6 O 7 R 8 G 9 E 10 E 11 K 12 S dtype: object
Creating Series from dictionary
Dictionary of some key and value pair for the series of values taking keys as index of series.
# import pandas as pd import pandas as pd
# simple dict dct = { 'G' : 2 , 'E' : 4 , 'K' : 2 , 'S' : 2 ,
'F' : 1 , 'O' : 1 , 'R' : 1 }
# forming series s = pd.Series(dct)
# output print (s)
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Output :
G 2 E 4 K 2 S 2 F 1 O 1 R 1 dtype: int64
Creating Series from Numpy array
The 1-D Numpy array of some values form the series of that values uses array index as series index.
# import pandas as pd import pandas as pd
# import numpy as np import numpy as np
# numpy array arr = np.array([ 'G' , 'E' , 'E' , 'K' , 'S' , 'F' ,
'O' , 'R' , 'G' , 'E' , 'E' , 'K' , 'S' ])
# forming series s = pd.Series(arr)
# output print (s)
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Output :
0 G 1 E 2 E 3 K 4 S 5 F 6 O 7 R 8 G 9 E 10 E 11 K 12 S dtype: object